Monitorización y diagnóstico de centrales térmicas: desarrollo de un detector visual de estados estacionarios

The design and features of a Matlab® application, focused to providing support for data mining by serial time computing is presented. The input data come from both historical records from industrial (thermo-energetic) processes but also it can be generated by direct simulation through the Simulink application. The aim of this study is the monitorization of the different quasi-stationary states (QSS) in a power plant, in order to identify and perform the diagnosis of possible malfunctions. Up to 8 signals, linearly normalized and distributed can be visualized and the user, by means of two cursors, can select short windows of recorded signals. In this version, statistical data are computed, facilitating the static modeling which can be exported to an Excel file. It is an open software application allowing the implementation of new features. A particular command makes easier the dynamic modeling and its applicability is exemplified by analysis of times series from a particular 250 MWe thermal power plant.Se presenta el diseño y las prestaciones de una aplicación desarrollada en Matlab®, orientada a dar soporte de cálculo para el tratamiento de los valores medios aproximados de intervalos de tiempo que resultan de la selección visual de series temporales que es el formato con el que se consideran a los registros industriales. Los datos de entrada pueden provenir de registros históricos de procesos industriales (termo-energéticos) ó de aquellos generados mediante simulación directa a través de la aplicación Simulink. El objetivo de este estudio es la monitorización de los diferentes estados cuasi-estacionarios (QSS) en una central térmica, a fin de poder identificar y realizar la diagnosis de posibles fallos. Pueden ser visualizadas hasta 8 señales linealmente normalizadas y distribuidas y el usuario, mediante dos cursores, puede seleccionar ventanas cortas de señales almacenadas. En esta versión, se computan datos estadísticos que facilitan el modelado estático, los cuales podrán ser exportados a un fichero Excel. Es una aplicación abierta, por lo que permite la inclusión de nuevas prestaciones. Un comando específico facilita el modelado dinámico y su aplicabilidad se demuestra con un ejemplo de análisis de series temporales provenientes de una central térmica de 250 MWe

PCA y lógica fuzzy para la detección de eventos anormales de operación, en una planta de craqueo catalítico fluidizado FCC

En el presente artículo se aplican y ajustan algunas técnicas y metodologías para la detección de fallos en una planta de Cracking Catalítico Fluidizado (FCC) modelo UOP, inicialmente se realiza un análisis de confiabilidad que permite definir el nivel de criticidad de cada uno de los equipos e identificar modos de fallo potenciales y su efecto sobre la operación de la planta; con la información entregada por este análisis se establecen casos de estudio, como requerimientos para un sistema de supervisión, detección y clasificación de situaciones anómalas, que pueden llevar al proceso a una condición de fallo. Para estudiar los casos se simulan las condiciones con un modelo de operación de la unidad FCC y se detectan los fallos con una herramienta basada en PCA (Análisis de componentes principales) y Lógica Fuzzy; finalmente se ajusta la herramienta utilizando datos históricos del proceso en presencia del fallo

Multivariate statistical analysis of Hall-Héroult reduction cells : investigation and monitoring of factors affecting performance

Les cuves d'électrolyse utilisées pour la production aluminium sont soumises à des variations de la qualité des matières premières, à des perturbations diverses encourues en cours de production ou en cours de démarrage. Il est connu que ces perturbations ont un impact sur la durée de vie des cuves ainsi que sur l'efficacité de production, métallurgique et énergétique. L'amélioration des performances passe nécessairement par une meilleure compréhension des sources de variations. Plusieurs travaux ont été présentés jusqu'à présent par le biais d'études univariées entre les différents facteurs et les performances. Cependant, dans ces études, le comportement des cuves n'est pas étudié de manière multivariée, ce qui ne permet pas d'étudier les interactions entre les différentes variables. Cette thèse propose d'étudier les facteurs affectant les performances des cuves d'électrolyse, précisément la duré de vie, le rendement Faraday et la consommation énergétique, par le biais de méthodes statistiques multivariées (PCA et PLS). Premièrement, il est démontré que la durée de vie des cuves est expliquée à 72% en utilisant l'information provenant des préchauffages, des démarrages et de l'opération transitoire, démontrant ainsi l'effet de ces étapes sur la durée de vie des cuves. Cette étude est suivie d'une analyse des facteurs affectant l'efficacité de courant et la consommation énergétique des cuves. L'effet de la qualité de l'alumine, des anodes, des variables manipulées, et des variables d'états des cuves permet d'expliquer 50% des variations des performances. Cette étude démontre l'importance du contrôle de la hauteur de bain. Ainsi, une étude approfondie des facteurs affectant la hauteur de bain est effectuée. La composition du produit de recouvrement des anodes a un impact majeur sur la hauteur de bain. Malheureusement, il est présentement impossible de bien effectuer le suivi et le contrôle de cette composition puisque seulement quelques échantillons sont analysés quotidiennement. Afin de palier à ce manque, cette thèse présente une nouvelle approche, basée sur l'analyse d'image, pour prédire la composition du produit de recouvrement. Cette application faciliterait le suivi et le contrôle de la composition, ce qui améliorerait le contrôle de la hauteur de bain permettant ainsi d'améliorer les performances des cuves

Modeling innovativeness in consumer products with the influence of environmental sustainability

This thesis is the result of two research publications, which together form a framework for the evaluation of product innovativeness with potential future applications to early design. Innovation is a challenging concept to quantify. Experts seemingly have a grasp of what innovation is when they see it, however this evaluation is highly subjective. To date, there is no accurate way of measuring a product's innovativeness. The research presented herein outlines a method to quantify product innovativeness based on three latent, or unobserved, variables. These latent variables were calculated based on several product attributes. The values of these measurable product attributes were associated with the probability that a product was selected as innovative. One attribute, product sustainability, is measured by environmental impact. As there was no environmental impact data available for the products used to generate the latent variable model, experiments were designed to collect such data. Life cycle assessment was performed on the products, as well as several other pairs of innovative and common products. These product's environmental impacts were compared at a high level with the entire product's impact, as well as at a more abstract level, with the functions and flows of each product. It was found that innovative products tend to have a higher environmental impact, which was concentrated in a small number of functions. With functional environmental impacts calculated, it will be possible to evaluate the environmental impact of designs during the conceptual stages of design, when only a functional model is available. In addition, the framework for evaluating product innovativeness, using a latent variable model, can be used during the original design process to evaluate how innovative concepts or designs are. Together, these two evaluations have the capability of producing innovative and more sustainable products. Future directions for this research include the application of the framework in an original design to determine whether more innovative concepts can be generated. Additionally, environmental impacts of conceptual designs and functional architectures can be estimated to determine which concepts to pursue in detailed design. Comparing alternatives in functional designs will help to determine the conceptual design with the lowest environmental impact to pursue in detailed design

Gaussian process models for proces monitoring and control

PhD ThesisOne problem of special interest both in industry and the engineering community is that of using the enormous amounts of data routinely generated and recorded in e cient process monitoring and control strategies. In statistical terms this is related to identifying those variables which exhibit unwanted or unusual process variability so that remedial action can be taken. To this end, a common approach in the literature is to reduce the problem dimensionality by using latent variable models. Customarily, the latent variables are a function of all of the original variables and monitoring is carried out in the reduced space. Within this context, this thesis explores the development of models in which the latent factors are a function of a subset, only, of the original observations. By doing that, the advantages of monitoring in a reduced subspace are retained but there there are also additional gains in model interpretability. The idea arises from the sparse representation of the mapping matrix between latent and original variables in a linear factor analysis (FA) model. An extension of principal component analysis (PCA) to monitor nonlinear systems is proposed by using a a Gaussian Process Latent Variable model [Lawrence, 2005], GPLVM, as a starting point. Its application in a process control problem is also introduced. Using a Gaussian process, GP, as the backbone, we de ne a Gaussian Process Functional Factor Analysis model which maps subsets of the latent variables to the observed data-space; a study of the model asymptotic properties is given. Several parameter inference methods as well as a model selection procedure via penalty functions are also proposed. There are several scienti c disciplines involved in the problem at hand. Chemical engineers refer to it as a sub- eld of Process Control known as Multivariate Statistical Process Control. It is also an area of tremendous success in process Chemometrics where it has grown very rapidly over the last two decades. In Statistics, it touches the topics of latent variable models and variable selection methods. And within the Machine Learning community is classi ed as an Unsupervised Learning problem.The Engineering and Physical Sciences Research Council(EPSRC): British Petroleum (BP)

Multivariate statistical process monitoring using classical multidimensional scaling

A new Multivariate Statistical Process Monitoring (MSPM) system, which comprises of three main frameworks, is proposed where the system utilizes Classical Multidimensional Scaling (CMDS) as the main multivariate data compression technique instead of using the linearbased Principal Component Analysis (PCA). The conventional method which usually applies variance-covariance or correlation measure in developing the multivariate scores is found to be inappropriately used especially in modelling nonlinear processes, where a high number of principal components will be typically required. Alternatively, the proposed method utilizes the inter-dissimilarity scales in describing the relationships among the monitored variables instead of variance-covariance measure for the multivariate scores development. However, the scores are plotted in terms of variable structure, thus providing different formulation of statistics for monitoring. Nonetheless, the proposed statistics still correspond to the conceptual objective of Hotelling’s T2 and Squared Prediction Errors (SPE). The first framework corresponds to the original CMDS framework, whereas the second utilizes Procrustes Analysis (PA) functions which is analogous to the concept of loading factors in PCA for score projection. Lastly, the final framework employs dynamic mechanism of PA functions as an alternative for enhancing the procedures of the second approach. A simulated system of Continuous Stirred Tank Reactor with Recycle (CSTRwR) has been chosen for the demonstration and the fault detection results were comparatively analyzed to the outcomes of PCA on the grounds of false alarm rates, total number of detected cases and also total number of fastest detection cases. The last two performance factors are obtained through fault detection time. The overall outcomes show that the three CMDS-based systems give almost comparable performances to the linear PCA based monitoring systemwhen dealing the abrupt fault events, whereas the new systems have demonstrated significant improvement over the conventional method in detecting incipient fault cases. More importantly, this monitoring accomplishment can be efficiently executed based on lower compressed dimensional space compared to the PCA technique, thus providing much simpler solution. All of these evidences verified that the proposed approaches are successfully developed conceptually as well as practically for monitoring while complying fundamentally with the principles and technical steps of the conventional MSPM system.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Multivariate statistical process monitoring using classical multidimensional scaling

A new Multivariate Statistical Process Monitoring (MSPM) system, which comprises of three main frameworks, is proposed where the system utilizes Classical Multidimensional Scaling (CMDS) as the main multivariate data compression technique instead of using the linearbased Principal Component Analysis (PCA). The conventional method which usually applies variance-covariance or correlation measure in developing the multivariate scores is found to be inappropriately used especially in modelling nonlinear processes, where a high number of principal components will be typically required. Alternatively, the proposed method utilizes the inter-dissimilarity scales in describing the relationships among the monitored variables instead of variance-covariance measure for the multivariate scores development. However, the scores are plotted in terms of variable structure, thus providing different formulation of statistics for monitoring. Nonetheless, the proposed statistics still correspond to the conceptual objective of Hotelling’s T2 and Squared Prediction Errors (SPE). The first framework corresponds to the original CMDS framework, whereas the second utilizes Procrustes Analysis (PA) functions which is analogous to the concept of loading factors in PCA for score projection. Lastly, the final framework employs dynamic mechanism of PA functions as an alternative for enhancing the procedures of the second approach. A simulated system of Continuous Stirred Tank Reactor with Recycle (CSTRwR) has been chosen for the demonstration and the fault detection results were comparatively analyzed to the outcomes of PCA on the grounds of false alarm rates, total number of detected cases and also total number of fastest detection cases. The last two performance factors are obtained through fault detection time. The overall outcomes show that the three CMDS-based systems give almost comparable performances to the linear PCA based monitoring systemwhen dealing the abrupt fault events, whereas the new systems have demonstrated significant improvement over the conventional method in detecting incipient fault cases. More importantly, this monitoring accomplishment can be efficiently executed based on lower compressed dimensional space compared to the PCA technique, thus providing much simpler solution. All of these evidences verified that the proposed approaches are successfully developed conceptually as well as practically for monitoring while complying fundamentally with the principles and technical steps of the conventional MSPM system.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Estrategias de análisis y exploración de datos como soporte a la operación y supervisión deprocesos químicos

En esta tesis se presenta un conjunto de metodologías que intentan facilitar la tarea de explotación de la información contenida en los datos históricos de proceso y como reaprovecharlos de modo de producir un impacto positivo en la operación del proceso.Se comienza por atacar el problema de asegurar la calidad de los datos. Se hace una revisión de los métodos de filtración univariable, en especial los basados en técnicas wavelets, ya que estos últimos se han mostrado en la literatura particularmente ventajosos para el filtrado de datos. Se establece, mediante experimentos, cuales son las funciones wavelets más apropiadas para el filtrado de diversos patrones de señales. Luego, se propone una mejora a los métodos actuales de filtración con wavelets por añadir un paso previo de estimación del nivel de descomposición que afecta a la aplicación de las wavelets. Lo anterior ayuda a una mayor autonomía en la aplicación en línea de estos métodos, a la vez que aseguran precisión en la estimación de los filtrados resultantes. Adicionalmente, se propone una estrategia que combina varias wavelets para intentar dar respuesta en aplicaciones en-línea a la pregunta de cual wavelets utilizar.El problema de la calidad de los datos también se estudia a través del enfoque de Reconciliación de Datos (RD). Se intenta contribuir al desarrollo de estrategias para casos dinámicos y lineales, uno de los retos actuales de la RD. La propuesta desarrollada combina un paso inicial de extracción de tendencias mediante filtrado basado en wavelets con la posterior reconciliación de las tendencias con una técnica RD basada en la representación polinómica del modelo del proceso. Las propuestas se muestran mejor que las estrategias actuales, en términos de precisión de los estimados obtenidos. Adicionalmente, se propone una primera extensión del método para procesos altamente no lineales, obteniéndose resultados satisfactorios.En el área de supervisión se presenta un análisis comparativo de diversas estrategias de monitorización basada en Análisis de Componentes Principales (ACP) y para el caso de procesos afectados frecuentemente por perturbaciones de lenta aparición. Se propone una variante basada en filtrado wavelets con ACP que logra obtener respuestas competitivas con los métodos actuales para la detección de este tipo de perturbaciones pero que, adicionalmente, reduce drásticamente la generación de alarmas falsas.En otro bloque de trabajos de supervisión se presenta el análisis de estrategias que combina ACP con técnicas de Clustering, para la supervisión de procesos multioperacionales. En una primera parte se presenta una comparación de diversas combinaciones ACP-clustering. Esta comparación permite establecer cual de ellas brinda un mejor manejo de aspectos como la identificación de clusters de formas diversas ó el tratamiento de outliers. En la comparación se añaden leves extensiones a algunas de las técnicas existentes que conducen a un mejor manejo de todos los aspectos mencionados anteriormente. Adicionalmente, se establecen alternativas de cómo usar las técnicas para casos en que se tiene poco ó ningún conocimiento previo de los grupos de operación. A continuación, se propone una integración TEM-clustering con modelos ACP multigrupos para la supervisión de procesos multi-operacionales. A diferencia de las estrategias existentes en la literatura, se introduce el tratamiento de las transiciones durante la etapa de diseño del sistema de monitorización, como soporte al operador durante un cambio de operaciones y/o para ayudarle a reducir rápidamente el espejo de posibles causas de anormalidades tras la ocurrencia de un fallo. Finalmente, las estrategias anteriores se adaptan al análisis de procesos afectados por decaimiento en la operación. La estrategia resultante se muestra potencialmente útil tanto para profundizar en el conocimiento del proceso como para asistir en su supervisión, planificación y mantenimiento.This thesis presents a set of new methodologies that tries to exploit the information embedded in process historical data and effectively support process analysis and supervision tasks.The data rectification problem was considered in first place. The adequacy of some type of wavelet for univariate filtering of different signal patterns was studied. Then, a strategy to determine the best decomposition level was proposed and consequently, an initial step to improve current wavelet filtering approaches was found. The obtained results expand the applicability and reliability of existing filtering schemes with wavelets for on-line applications without losing of accuracy on signal estimation. Additionally, an alternative strategy was proposed to solve the problem of which wavelet to choose. This last strategy consist on a weighted combination of different wavelets functions with only one output. The data rectification problem was also studied through a Data Reconciliation (DR) approach. The focus was set on DR developments for Dynamics and Linear Systems. The proposed strategy consists on first applying a trend extraction step, to identify measured process variables trends and then reconciling these trends to make them consistent with the dynamic process model studied. For the trend extraction step, filtering using wavelets was adopted. To reconcile the estimated variables trends, a extended polynomial approach was used. The comparison with existing RD approaches shows promising results in terms of accuracy and computing efficiency. Further extensions that contemplate nonlinear cases were also introduced, showing also satisfactory results.Process Supervision problems were considered in second place. Primarily, Principal Components Analysis (PCA) based monitoring strategies for treatment of processes frequently affected by slowly appearing disturbances or small relative shifts were compared. This comparison included some new proposals combining wavelets filtering approaches and PCA. One of the proposed approaches was capable of handling the detection of small disturbances as good as other existing approaches, but dramatically reducing the problem of false alarms generation.Multioperational process supervision strategies were also considered and studied. First, a comparison of different strategies from literature was considered. The aim was to determine the strategy that produced better results in front of issues like identification of clusters with different forms or its performance facing outliers. The considered strategies are based on the combination of PCA with clustering techniques (PCA-clustering). Not only existing approaches were studied but also some extensions of them were also considered. Finally it was shown how new modified strategies lead to improve handling of all the considered issues. In addition, cluster number estimation problem was studied and some successfully strategies were proposed to perform it.Finally, the integration of the above PCA-clustering strategies with multigroup PCA for supervising of multioperational process was proposed and evaluated. The aim was to allow good process supervision capabilities for handling operating changes situations and to facilitate fault diagnosis tasks together with additional capabilities like data transitions treatment. Additionally, an extension of the above-integrated strategy for analysis and supervision of process with decaying performance was evaluated. The resulting strategy was shown as potentially useful to extract useful knowledge from data and to support supervising, planning and maintenance process tasks